title: Genome-scale prediction of proteins with long intrinsically disordered regions.
abstract: Proteins with long disordered regions (LDRs), defined as having 30 or more consecutive disordered residues, are abundant in eukaryotes, and these regions are recognized as a distinct class of biologically functional domains. LDRs facilitate various cellular functions and are important for target selection in structural genomics. Motivated by the lack of methods that directly predict proteins with LDRs, we designed Super-fast predictor of proteins with Long Intrinsically DisordERed regions (SLIDER). SLIDER utilizes logistic regression that takes an empirically chosen set of numerical features, which consider selected physicochemical properties of amino acids, sequence complexity, and amino acid composition, as its inputs. Empirical tests show that SLIDER offers competitive predictive performance combined with low computational cost. It outperforms, by at least a modest margin, a comprehensive set of modern disorder predictors (that can indirectly predict LDRs) and is 16 times faster compared to the best currently available disorder predictor. Utilizing our time-efficient predictor, we characterized abundance and functional roles of proteins with LDRs over 110 eukaryotic proteomes. Similar to related studies, we found that eukaryotes have many (on average 30.3%) proteins with LDRs with majority of proteomes having between 25 and 40%, where higher abundance is characteristic to proteomes that have larger proteins. Our first-of-its-kind large-scale functional analysis shows that these proteins are enriched in a number of cellular functions and processes including certain binding events, regulation of catalytic activities, cellular component organization, biogenesis, biological regulation, and some metabolic and developmental processes. A webserver that implements SLIDER is available at http://biomine.ece.ualberta.ca/SLIDER/.
authors.affiliation: Department of Electrical and Computer Engineering, University of Alberta, Edmonton, Canada.
authors.forename: Zhenling
authors.initials: Z
authors.lastname: Peng
authors.forename: Marcin J
authors.initials: MJ
authors.lastname: Mizianty
authors.forename: Lukasz
authors.initials: L
authors.lastname: Kurgan
mesh_terms: D000465:Algorithms; D000596:Amino Acids / Q000235:genetics; D030562:Databases, Protein; D020407:Internet; D016015:Logistic Models; D011487:Protein Conformation; D011506:Proteins / Q000737:chemistry* / Q000235:genetics*; D040901:Proteomics / Q000379:methods*; D012984:Software
chemical_list: D000596:Amino Acids; D011506:Proteins
keywords: disorder prediction; eukaryotes; high-throughput prediction; intrinsic disorder; long disordered regions